1. Field of Invention
The present invention relates to methods for detecting and quantifying a cerebral infarct. More particularly, the present invention relates to automatic methods for detecting and quantifying a cerebral infarct.
2. Description of Related Art
In recent years, the stroke incidence and mortality of people remain high, and the stroke has been ranked as the top 10 causes of death, indicating that the stroke is a major threat to people's health. The stroke is the loss of brain function due to a disturbance in the blood supply to the brain, which is caused by ischemia, i.e. lack of blood flow, or hemorrhage. Ischemic stroke is caused by thrombotic or embolic occlusion of a cerebral artery, and hemorrhagic stroke is caused by bleeding into the brain. In clinical, most stroke patients suffer from the ischemic stroke, and generally have cerebral infarct.
There are two major medical methods for detecting a cerebral infarct, which are computed tomography (CT) and magnetic resonance imaging (MRI), and MRI is more widely used among the two. The signal of MRI comes from the resonance of hydrogen atoms presented in water molecules inside the brain. When infarct caused by ischemia occurred, water molecules inside the brain tissue change, and thereby MRI can detect the changes of signal intensity, which is applied in the detection and treatment for cerebral infarct. With the progress of MRI technology, in addition to traditional images, such as T1-weighted image, that can understand anatomical structure of tissue, the delicate structure of tissue and its functional components can be further understood by diffusion-weighted imaging (DWI).
The method for detecting acute cerebral infarct in hospital is semi-automatic segmentation method assisted by software, which is time-consuming for processing and analysis, and is prone to produce variability between different raters, i.e. doctors. Automatic algorithms for segmentation of cerebral infarct have been proposed in the past, such as Li et al. “Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification.” Neuroimage. 2004 December; 23(4):1507-18, Prakash et al. “Identification, segmentation, and image property study of acute infarcts in diffusion-weighted images by using a probabilistic neural network and adaptive gaussian mixture model.” Acad Radiol. 2006 December; 13(12):1474-84, and Shen et al. “Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location—a 3-D automatic approach.” IEEE Trans Inf Technol Biomed. 2008 July; 12(4):532-40. However, it is difficult to accurately detect the cerebral infarct by using these methods due to noise, signal overlap, partial volume effect (PVE), and artifacts caused by magnetic inhomogeneity.
Therefore, there is a need for methods for detecting and quantifying cerebral infarct, which can not only reduce the variability of human judgment but also detect the cerebral infarct rapidly and accurately, especially for acute cerebral infarct, which needs a rapid, real-time, and accurate detecting method.